In [1]:
import plotly.offline as pyo

from plotly.graph_objs import *

import chart_studio.plotly as py

import pandas as pd
from pandas import DataFrame
In [2]:
pyo.offline.init_notebook_mode()
In [3]:
outcomes = pd.read_csv(r"../Data/StudentOutcomes201415BySubjectArea.csv", index_col = 0)
outcomes
Out[3]:
Work Work and further study Further study Unemployed Other
Medicine & dentistry 6855.0 135.0 345.0 15.0 35.0
Subjects allied to medicine 22135.0 935.0 1825.0 620.0 630.0
Biological sciences 15725.0 1800.0 6045.0 1520.0 1335.0
Veterinary science 550.0 10.0 20.0 5.0 10.0
Agriculture & related subjects 1265.0 120.0 195.0 120.0 145.0
Physical sciences 6510.0 555.0 3410.0 920.0 620.0
Mathematical sciences 2910.0 360.0 1230.0 385.0 265.0
Computer science 7220.0 225.0 810.0 925.0 310.0
Engineering & technology 9420.0 405.0 1740.0 1010.0 580.0
Architecture, building & planning 3345.0 230.0 380.0 270.0 170.0
Total - Science subject areas 75930.0 4770.0 15995.0 5780.0 4110.0
Social studies 14925.0 1355.0 3795.0 1465.0 1380.0
Law 4470.0 995.0 2870.0 485.0 425.0
Business & administrative studies 20150.0 1500.0 2295.0 1790.0 1375.0
Mass communications & documentation 5210.0 165.0 455.0 520.0 355.0
Languages 9340.0 940.0 3165.0 975.0 870.0
Historical & philosophical studies 6140.0 715.0 2640.0 705.0 635.0
Creative arts & design 19925.0 920.0 2170.0 1865.0 1290.0
Education 9590.0 370.0 1485.0 285.0 400.0
Combined 245.0 40.0 80.0 25.0 35.0
Total 165930.0 11765.0 34950.0 13900.0 10875.0
In [4]:
fig = {'data' : [{'type' : 'pie',
          'labels' : outcomes.columns.tolist(),
          'values' : outcomes.loc['Medicine & dentistry'],
          'name' : 'Medicine & dentistry',
          'direction' : 'clockwise'}],
       'layout' : {'title' : 'Outcomes for Medicine and Dentistry Students'}}

pyo.iplot(fig)
In [5]:
fig['data'][0].update({'hole' : 0.9})
pyo.iplot(fig)
In [6]:
fig['data'][0].update({'hole' : 0.5})
pyo.iplot(fig)
In [8]:
info = "Medicine & Dentistry students are more likely to be employed than students from any other subject area"
fig['layout'].update({'annotations' : [{'text' : info,
                                       'xref' : 'paper',
                                       'yref' : 'paper',
                                       'x' : 0.5,
                                       'y' : 0.5,
                                       'showarrow' : False}]})
pyo.iplot(fig)
In [9]:
info = "<b>Medicine &<br>Dentistry students<br>are more likely to<br>be employed than<br>students from any<br>other subject<br>area</b>"
fig['layout'].update({'annotations' : [{'text' : info,
                                       'xref' : 'paper',
                                       'yref' : 'paper',
                                       'x' : 0.5,
                                       'y' : 0.5,
                                       'showarrow' : False,
                                       'font' : {'size' : 16}}]})
pyo.iplot(fig)
In [10]:
fig['data'][0].update({'hole' : 0.55})
pyo.iplot(fig)
In [ ]: